timescapes-co Verified 44h ago
Machine Learning Engineer
Melbourne, Australia On-site
Salary not listedPaySalary not listed
TypeFull-time
Work settingOn-site
Verified listing
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About the role
Timescapes is looking for a Machine Learning Engineer to join our small, but growing team of high-performers. As our second dedicated ML / AI hire at Timescapes, you’ll be working closely with our product and engineering team to develop ML models (computer vision) and incorporate them into our existing (and new) product capabilities. Key aspects of the role include:
What you'll bring
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Experience with the development and delivery of production-level software systems that feature critical machine learning components.
- Experience with the development of computer vision models, from the data side as well as the model side.
- Experience with end-to-end ML operations, including data acquisition, labeling, model training, pipeline design + management, continuous integration, model versioning, and performance monitoring.
- Experience with leveraging LLMs, LVMs and multimodality models to augment product capabilities, including strong experimentation skills, and the ability to combine off-the-shelf commercial model capabilities with custom ML models.
- Familiarity with methods for ML model validation and verification.
- Experience deploying ML inference infrastructures for systems with high throughput.
- Ability to develop ML-enabled product capabilities in the context of the entire SDLC, from initial concepts through detailed design, development, deployment and maintenance.
- We want curious, driven and enthusiastic people who are motivated by the prospect of building solutions that endure.
- The people who thrive at Timescapes are critical thinkers who are not afraid to embrace the unconventional.
- Value-oriented: we’re focussed on ensuring that Timescapes as a business gets genuine value from engineering investments.
- You’ll be someone who sees methodologies, best practices and patterns as useful tools, but you don’t blindly adopt them and never let them get in the way of great customer outcomes.
Benefits
Employee Assistance ProgramMedical Insurance